Appalachians are the most sedentary population in the U.S.; teens are particularly sedentary. Only 13.6% of teens reported 60 minutes of daily moderate activity, while 38% reported no moderate physical activity and 78.2% reported no vigorous physical activity in the past week. The long-term goal of this study is to positively impact the physical activity patterns to improve health outcomes including the high rates of obesity in Appalachian teens. Our approach will train peer mentors to deliver the culturally appropriate intervention and provide social support that is critical for facilitating and sustainin health behavior change. Our objective is to compare the efficacy of an innovative healthy lifestyle skills mentoring program (Mentored Planning to be Active [MBA]) to a teacher led program (PBA) for increasing physical activity in Appalachian high school teens. MBA emphasizes the social determinants of health by using a social networking approach that trains peer mentors to support targeted teens. We will test the hypothesis that, compared to delivery by teachers in a classroom setting, an innovative delivery format of PBA by local peer mentors will promote the adoption of healthier physical activity and regular exercise among teens by combining peer mentoring with a tailored self-regulation lifestyle program. This study will guide the development of effective interventions (currently lacking) specifically targeting residents of Appalachia, a region with disproportionately high prevalence rates of childhood obesity and significant challenges to achieving healthy lifestyles. We propose a group-randomized controlled trial (G-RCT) to evaluate mentored delivery in Appalachian Ohio. We will recruit high schools in 2 waves, with 10 in Wave 1 and 10 in Wave 2, for a total of 20 schools. For each wave of 10 schools, we will randomly assign 5 schools to each condition--intervention (MBA]) and comparison (PBA)--for a total of 10 schools in each of the two conditions by study's end. We will collect data at baseline (T1), 3 months post intervention (T2), and 6 months post intervention (T3). Positive intra-class correlation is expected among observations from students in the same school. Ignoring positive ICC can inflate Type 1 error rate. We will avoid these problems by analytic methods appropriate to the structure of the design and data. Specifically, we will use Linear Mixed Models and Generalized Linear Mixed Models to account for various levels of correlation among subjects. Such an approach is also known as a mixed-model ANCOVA. Power for this study was based on power for the primary analysis comparing BMI outcomes at T2 between the two groups. We will implement these models using SAS PROC MIXED and GLIMMIX, Version 9.3.